Abstract book (pdf) - ICPR 2010

Mezghani, Neila, Centre de Recherche du CHUM
Phan, Philippe, Sainte-Justine University Hospital Center
Mitiche, Amar, Labella, Hubert, cole Polytechnique de Montreal
de Guise, Jacques, Centre de Recherche du CHUM
Surgical instrumentation for the Adolescent idiopathic scoliosis (AIS) is a complex procedure involving many difficult
decisions. Selection of the appropriate fusion level remains one of the most challenging decisions in scoliosis surgery.
Currently, the Lenke classification model is generally followed in surgical planning. The purpose of our study is to investigate
a computer aided method for Lenke classification and scoliosis fusion level selection. The method uses a self organizing
neural network trained on a large database of surgically treated AIS cases. The neural network produces two
maps, one of Lenke classes and the other of fusion levels. These two maps show that the Lenke classes are associated
with the the proper fusion level categories everywhere in the map except at the Lenke class transitions. The topological
ordering of the Cobb angles in the neural network justifies determining a patient scoliotic treatment instrumentation using
directly the fusion level map rather than via the Lenke classification.
14:50-15:10, Paper ThBT7.5
A Fast and Robust Graph-Based Approach for Boundary Estimation of Fiber Bundles Relying on Fractional
Anisotropy Maps
Bauer, Miriam Helen Anna, Univ. of Marburg
Egger, Jan, Univ. of Marburg
Odonnell, Thomas Patrick, Siemens Corp. Res.
Freisleben, Bernd, Univ. of Marburg
Barbieri, Sebastiano, Fraunhofer MEVIS
Klein, Jan, Fraunhofer MEVIS
Hahn, Horst Karl, Fraunhofer MEVIS
Nimsky, Christopher, Univ. Marburg
In this paper, a fast and robust graph-based approach for boundary estimation of fiber bundles derived from Diffusion
Tensor Imaging (DTI) is presented. DTI is a non-invasive imaging technique that allows the estimation of the location of
white matter tracts based on measurements of water diffusion properties. Depending on DTI data, the fiber bundle boundary
can be determined to gain information about eloquent structures, which is of major interest for neurosurgery. DTI in combination
with tracking algorithms allows the estimation of position and course of fiber tracts in the human brain. The presented
method uses these tracking results as the starting point for a graph-based approach. The overall method starts by
computing the fiber bundle centerline between two user-defined regions of interests (ROIs). This centerline determines
the planes that are used for creating a directed graph. Then, the mincut of the graph is calculated, creating an optimal
boundary of the fiber bundle.
ThCT1 Marmara Hall
Object Detection and Recognition - VI Regular Session
Session chair: Denzler, Joachim (Friedrich-Schiller Univ. of Jena )
15:40-16:00, Paper ThCT1.1
Recognizing 3D Objects with 3D Information from Stereo Vision
Yoon, Kuk-Jin, GIST
Shin, Min-Gil, GIST
Lee, Ji-Hyo, Samsung Electronics
Conventional local feature-based object recognition methods try to recognize learned 3D objects by using unordered local
feature matching followed by the verification. However, the matching between unordered feature sets can be ambiguous
and, moreover, it is difficult to deal with general shaped 3D objects in the verification stage. In this paper, we present a
new framework for general 3D object recognition, which is based on the invariant local features and their 3D information
with stereo cameras. We extend the conventional object recognition framework for stereo cameras. Since the proposed
method is based on the stereo vision, it is possible to utilize 3D information of local features visible from two cameras.
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